code: def test_isotonic_regression_clipping():
      X_full, y_full = make_regression(
          n_samples=10000, n_features=1, random_state=41, noise=0.8
      )
      X_full = X_full.reshape(-1)
  
      p05 = np.quantile(X_full, 0.05)
      p95 = np.quantile(X_full, 0.95)
  
      X = X_full[np.logical_and(p05 < X_full, X_full < p95)]
      y = y_full[np.logical_and(p05 < X_full, X_full < p95)]
  
      # run sklearn Isotonic Regression to extract thresholds
      iso_reg = IsotonicRegression(out_of_bounds="clip").fit(X, y)
  
      # now invoke H2O Isotonic Regression
      train = H2OFrame(np.column_stack((y, X)), column_names=["y", "X"])
      h2o_iso_reg = H2OIsotonicRegressionEstimator(out_of_bounds="clip")
      h2o_iso_reg.train(training_frame=train, x="X", y="y")
  
      test = H2OFrame(
          np.column_stack((y_full, X_full)), column_names=["y", "X"]
      )
      h2o_test_preds = h2o_iso_reg.predict(test).as_data_frame()
  
      test_preds = pd.DataFrame(
          iso_reg.predict(X_full), columns=["predict"]
      )
      assert_frame_equal(test_preds, h2o_test_preds)
  
file_path: h2o-3/h2o-py/tests/testdir_algos/isotonic/pyunit_isotonic_clipping.py
imports: import sys
  from tests import pyunit_utils
  import numpy
  import pandas
  from pandas.testing import assert_frame_equal
  from sklearn.datasets import make_regression
  from sklearn.isotonic import IsotonicRegression
  from h2o import H2OFrame
  from h2o.estimators.isotonicregression import H2OIsotonicRegressionEstimator
label_desc: Use image node_id 1 for calling a global function with example usage: test_isotonic_regression_clipping() without return types